Load libraries, such as tidyverse and tidybiology

Import data

The data imported was sereis of measurements of zebrafish larvae, which was oiginally collected by me in the Rawls Lab.

Take a glimpse into the data in the dataframe

The data frame consist of different morphological measurements of zebrafish larvae including their corresponding KO genes

## # A tibble: 247 x 9
##     Fish Mutant    SL   HAA  area    AT Adiposity gene  Allele    
##    <dbl> <chr>  <dbl> <dbl> <dbl> <dbl>     <dbl> <chr> <chr>     
##  1     1 Homo    15.0  2.52  38.9  5.95     15.3  klf15 klf15(-/-)
##  2     2 WT      12.4  1.81  25.5  2.13      8.36 klf15 klf15(+/+)
##  3     3 Homo    13.5  2.16  30.7  1.95      6.34 klf15 klf15(-/-)
##  4     4 Hets    14.8  2.46  36.6  5.14     14.0  klf15 klf15(+/-)
##  5     5 WT      14.3  2.16  32.9  2.97      9.01 klf15 klf15(+/+)
##  6     6 WT      10.5  1.47  17.4  1.71      9.85 klf15 klf15(+/+)
##  7     7 Homo    15.5  2.95  44.2  5.28     12.0  klf15 klf15(-/-)
##  8     8 Homo    13.8  2.17  31.1  3.02      9.71 klf15 klf15(-/-)
##  9     9 WT      14.3  2.36  36.0  2.49      6.93 klf15 klf15(+/+)
## 10    10 WT      13.6  2.33  33.3  3.35     10.1  klf15 klf15(+/+)
## # … with 237 more rows

Zebrafish BMI

I started by exploring the relationship between larvae length and their 2D surface area as a proxy for BMI. Since many of these fish contain genetic mutation associated with obesogenic mutations identified in GWAS.

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Different mutants BMI comparison

Observation

It is clear that there are general trends of some mutants within the genotype that developes differently such as scd. But I wanted to see if more clusters can be identified more obviously when comparing multiple parameters.

Final Graph

I chose the 3D scatterplot method to visualize the difference between each fish’s measurment and their genotype. This method is especially powerful as it allows me to identify different cluster and deviations of groups from the wild type.

Conclusions

From the 3D data set shown, we can observe different groups of cluster formed and deviated from the normal zebrafish developmental trajectory. Demonstrating possible phenotype that may be interesting.

Prioritized follow-up studies

Potential future study may involve investigating different mutants, and analyzing the metabolic rate or how the maturaty compares to the wild type animals.

Acknolwedgements

library(ggplot2)
library(plotly)
library(readxl)

Allie Mills
Ph.D. Akshay Bareja D.Phil
Matthew Hirschey
Ph.D. John Rawls Ph.D.

#Session information for provenance and reproducibility

## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/atlas-base/atlas/libblas.so.3.0
## LAPACK: /usr/lib/atlas-base/atlas/liblapack.so.3.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] readxl_1.3.1  plotly_4.9.0  ggplot2_3.2.1